text: # Copyright 2021 Condenser Author All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import os
  from contextlib import nullcontext
  
  from typing import Dict, List, Tuple, Optional, Any, Union
  
  import torch
  import torch.distributed as dist
  from torch import nn, Tensor
  from torch.cuda.amp import autocast
  from transformers.trainer import Trainer
  try:
      from grad_cache import GradCache
      _grad_cache_available = True
  except ModuleNotFoundError:
      _grad_cache_available = False
  
  import logging
  
  logger = logging.getLogger(__name__)
  
  
  class CondenserPreTrainer(Trainer):
      def _save(self, output_dir: Optional[str] = None):
          output_dir = output_dir if output_dir is not None else self.args.output_dir
          os.makedirs(output_dir, exist_ok=True)
          logger.info("Saving model checkpoint to %s", output_dir)
          # Save a trained model and configuration using `save_pretrained()`.
          # They can then be reloaded using `from_pretrained()`
          if not hasattr(self.model, 'save_pretrained'):
              raise NotImplementedError(
                  f'MODEL {self.model.__class__.__name__} '
                  f'does not support save_pretrained interface')
          else:
              self.model.save_pretrained(output_dir)
          if self.tokenizer is not None and self.is_world_process_zero():
              self.tokenizer.save_pretrained(output_dir)
  
          # Good practice: save your training arguments together with the trained model
          torch.save(self.args, os.path.join(output_dir, "training_args.bin"))
  
      def _remove_unused_columns(self, dataset, description: Optional[str] = None):
          # we are not going to do this in this
          # as collator will be generating new columns
          pass
  
      def create_optimizer_and_scheduler(self, num_training_steps: int):
          if self.args.warmup_ratio > 0:
              self.args.warmup_steps = num_training_steps * self.args.warmup_ratio
  
          super().create_optimizer_and_scheduler(num_training_steps)
  
      def compute_loss(self, model, inputs):
          labels = inputs.pop('labels')
          return model(inputs, labels)
  
      def prediction_step(
              self,
              model: nn.Module,
              inputs: Dict[str, Union[torch.Tensor, Any]],
              prediction_loss_only: bool,
              ignore_keys: Optional[List[str]] = None,
      ) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
  
          inputs = self._prepare_inputs(inputs)
          labels = inputs.pop('labels')
  
          if ignore_keys is None:
              if hasattr(self.model, "config"):
                  ignore_keys = getattr(self.model.config, "keys_to_ignore_at_inference", [])
              else:
                  ignore_keys = []
  
          with torch.no_grad():
              if self.args.fp16:
                  with autocast():
                      outputs = model(inputs, labels)
              else:
                  outputs = model(inputs, labels)
  
              loss = outputs
  
          return (loss, None, None)
  
  
  class CoCondenserPretrainer(CondenserPreTrainer):
      def __init__(self, *args, **kwargs):
          logger.info('Initializing Gradient Cache Trainer')
          super(CondenserPreTrainer, self).__init__(*args, **kwargs)
  
          if self.args.cache_chunk_size != -1:
              if not _grad_cache_available:
                  raise ValueError(
                      'Grad Cache package not available. You can obtain it from https://github.com/luyug/GradCache.')
              self.gc = GradCache(
                  models=[self.model.lm],
                  chunk_sizes=self.args.cache_chunk_size,
                  loss_fn=self.model.compute_contrastive_loss,
                  get_rep_fn=lambda x: x.hidden_states[-1][:, 0],
                  fp16=self.args.fp16,
                  scaler=self.scaler
              )
  
      def _gather_tensor(self, t: Tensor):
          all_tensors = [torch.empty_like(t) for _ in range(dist.get_world_size())]
          dist.all_gather(all_tensors, t)
          all_tensors[self.args.local_rank] = t
          return all_tensors
  
      def gather_tensors(self, *tt: Tensor):
          tt = [torch.cat(self._gather_tensor(t)) for t in tt]
          return tt
  
      def compute_loss(self, model, inputs, grad_cache=None, chunk_offset=None):
          labels = inputs.pop('labels')
          return model(inputs, labels, grad_cache=grad_cache, chunk_offset=chunk_offset)
  
      def split_tensor_dict(self, td: Dict[str, Tensor]):
          keys = list(td.keys())
          chunked_tensors = [td[k].split(self.args.cache_chunk_size) for k in keys]
          return [dict(zip(keys, tt)) for tt in zip(*chunked_tensors)]
  
      def training_step(self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]) -> torch.Tensor:
          if self.args.cache_chunk_size == -1:
              return super(CoCondenserPretrainer, self).training_step(model, inputs)
  
          model.train()
  
          inputs = self._prepare_inputs(inputs)
          labels = inputs.pop('labels')
  
          # Construct the gradient cache
          chunked_inputs = self.split_tensor_dict(inputs)
          for c in chunked_inputs:
              c['output_hidden_states'] = True
          cls_hiddens, rnd_states = self.gc.forward_no_grad(self.model.lm, chunked_inputs)
          if self.args.local_rank > -1:
              cls_hiddens = self.gather_tensors(cls_hiddens.contiguous())[0]
          grad_cache, total_loss = self.gc.build_cache(cls_hiddens)
          grad_cache = grad_cache[0]
          if self.args.local_rank > -1:
              total_loss = total_loss / dist.get_world_size()
  
          inputs['labels'] = labels
          chunked_inputs = self.split_tensor_dict(inputs)
  
          # Compute the full loss with cached gradients
          for local_chunk_id, chunk in enumerate(chunked_inputs):
              device_offset = max(0, self.args.local_rank) * self.args.per_device_train_batch_size * 2
              local_offset = local_chunk_id * self.args.cache_chunk_size
              chunk_offset = device_offset + local_offset
              with rnd_states[local_chunk_id]:
                  if self.use_amp:
                      with autocast():
                          lm_loss, surrogate = self.compute_loss(model, chunk, grad_cache, chunk_offset)
                  else:
                      lm_loss, surrogate = self.compute_loss(model, chunk, grad_cache, chunk_offset)
  
              if self.args.gradient_accumulation_steps > 1:
                  raise ValueError
  
              ddp_no_sync = self.args.local_rank > -1 and (local_chunk_id + 1 < len(chunked_inputs))
              with model.no_sync() if ddp_no_sync else nullcontext():
                  if self.use_amp:
                      (self.scaler.scale(lm_loss) + surrogate).backward()
                  elif self.use_apex:
                      raise ValueError
                  elif self.deepspeed:
                      raise ValueError
                  else:
                      (lm_loss + surrogate).backward()
              total_loss += lm_loss
          return total_loss.detach()
  